Analysis of the Spatiotemporal Heterogeneity and Influencing Factors of Regional Economic Resilience in China DOI Creative Commons
Qiuyue Zhang, Yili Lin,

Yu Cao

et al.

Entropy, Journal Year: 2024, Volume and Issue: 27(1), P. 23 - 23

Published: Dec. 31, 2024

This study estimates regional economic resilience in China from 2000 to 2022, focusing on resistance resilience, recovery and reorientation resilience. The entropy method, kernel density estimation, spatial Durbin model are applied examine the spatiotemporal evolution influencing factors. results show significant clustering, with stronger east weaker west. While has generally improved, disparities persist. Key factors such as human capital, urban hospitals, financial development, market consumption, environmental quality have a positive effect spillover effects. However, capital hospitals also negative indirect impact surrounding regions. influence of these varies across regions periods, indicating strong heterogeneity.

Language: Английский

A study of the coupling between the digital economy and regional economic resilience: Evidence from China DOI Creative Commons

Jingshan Gu,

Z. Liu

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(1), P. e0296890 - e0296890

Published: Jan. 19, 2024

The contemporary economic landscape has placed significant emphasis on the digital economy and resilience, progressively emerging as pivotal focal points for examining high-quality development of systems. However, there remains to be more research several critical topics. This includes characteristics coordinated between resilience systems their interdependence. In response, this study formulates a comprehensive evaluative framework regional grounded in intrinsic mechanisms both domains. It conducts thorough evaluation employing entropy weight-TOPSIS methodology. Additionally, leveraging coupling theory, coordination model’s degree serves foundational scrutinizing symbiotic advancement along with interdependent nature. sample comprises data from 31 provinces municipalities China (excluding Hong Kong, Macao, Taiwan) 2011 2020. Spatial autocorrelation Geodetector methodologies probe evolutionary traits driving factors underlying developmental relationship these two findings indicate an upward trajectory China’s annual index (from 0.233 0.458) 0.393 0.497). systems, measured 0.504 0.658 2020, demonstrate consistent growth pattern average increase 3.01%. These levels exhibit continuous improvement, zones manifesting hierarchical results within range [0.5, 0.8]. Notably, agglomeration evinces pronounced spatial positive correlation, while local Moran scattering are primarily concentrated localized migration leaps. Factors such foreign-funded enterprises’ total import export volume, online payment capability, fiber-optic cable length greatly influence relationship. contrast, other variables lower fluctuating weighted impact. establishes foundation synergistic effective Chinese region. Simultaneously, it offers valuable insights related subjects global contexts.

Language: Английский

Citations

9

Measurement and Influencing Factors of Regional Economic Resilience in China DOI Open Access
Xinyu Zhang,

Congying Tian

Sustainability, Journal Year: 2024, Volume and Issue: 16(8), P. 3338 - 3338

Published: April 16, 2024

The COVID-19 outbreak in 2020 has underscored the paramount importance of regional economies’ capacities to withstand and adapt external shocks. Enhancing economic resilience mitigating adverse impacts on both economy society have emerged as critical imperatives for ensuring sustainable development transformation national economy. This paper employs an improved counterfactual method measure index across 31 Chinese provinces cities from 2001 2021, coupled with empirical analysis using a dynamic panel model identify influencing factors resilience. Building upon this foundation, study delves into heterogeneous effects various different degrees marketization regions. Research Findings: (1) There been significant improvement levels China’s provinces, differences between regions far exceeding those levels, indicating substantial internal disparities. (2) Factors such index, industrial structure, level informatization, labor force size, quality, innovation capacity, degree government intervention all impact exhibit heterogeneity. Policy Recommendations: It is crucial address disparities while formulating strategies enhancing Regions should accelerate market-oriented reforms, promote rational mobility, strengthen investment human capital, foster innovation, adjust intervention.

Language: Английский

Citations

8

Analysis of regional resilience network from the perspective of relational and dynamic equilibrium DOI
Xinran Wang, Shan Xu, Ding Wang

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 425, P. 138859 - 138859

Published: Sept. 16, 2023

Language: Английский

Citations

11

Suppression or promotion: research on the impact of industrial structure upgrading on urban economic resilience DOI Creative Commons
Lu Zhang,

Guodong Lin,

Xiao Lyu

et al.

Humanities and Social Sciences Communications, Journal Year: 2024, Volume and Issue: 11(1)

Published: June 27, 2024

Abstract Industrial The upgrading of industrial structure, as the main means urban economic transformation, plays a crucial role in process achieving resilience construction. We conducted study on nonlinear impact mechanism structure based panel data from 267 prefecture-level and above-level cities above China 2008 to 2021, using globalization threshold variable. obtained results demonstrated following: (1) there existed significant relationship between rationalization resilience, with double effect. (2) A robustness test was performed by removing extreme values sample, controlling for time series individual interaction terms while considering control variables, which did not change basic conclusions model. This that regression model constructed this is robust reliable. (3) From regional heterogeneity perspective, varied among different regions. Notably, imposed effect eastern central regions, manifested an inverted U-shaped trend. In northeastern region, only single variable, still occurred left side curve, no observed western region.

Language: Английский

Citations

4

How Migration Networks Shape Economic Resilience: Evidence from China's Intercity Truck Flows during COVID-19 DOI
Haochen Zhang, Fang Da

Published: Jan. 1, 2025

Language: Английский

Citations

0

Spatial correlation network of Chinese-style ecological modernization and its influencing factors DOI Creative Commons
Huiping Wang, Yonghui Huang

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113297 - 113297

Published: March 1, 2025

Language: Английский

Citations

0

Study on the impact of China’s urban agglomerations’ tiered spatial structure on regional economic resilience DOI Creative Commons
Wei Liang,

Deqi Wang,

Luyao Gao

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0314538 - e0314538

Published: March 13, 2025

Urban agglomerations serve as crucial spatial carriers of economic development, and their structure profoundly influences regional resilience. This study draws on Martin’s conceptualization resilience and, considering the administrative hierarchy development stage China’s urban system, examines impact layered Based data from 17 Chinese 2005 to 2019, this research employs a one-step system Generalized Method Moments (GMM) model empirically analyze effects three mechanisms – polycentricity within agglomerations, inter-city disparities, industrial gradients The findings reveal that both polycentric distribution population economy coupling dual centers significantly positively affect agglomerations. potential energy difference between core peripheral cities can be transformed into developmental momentum for cities, thereby generating positive externalities play significant role in enhancing As ratio secondary tertiary industry employees nears 1, agglomerations’ strengthens, underscoring importance balanced gradient collaboration mitigating shocks. also considers heterogeneity whether are coastal they contain national-level central city. finds inland those with existing developing effectively enhance region’s responding various Furthermore, aside few more developed other should continue focus agglomeration, fostering strengthen competitiveness. Finally, given varying industrialization stages structures appropriately adjusting is essential.

Language: Английский

Citations

0

The Evolution and Configuration Mechanism of Spatial Correlation Network in China's Innovation Ecosystem DOI Creative Commons

WEN BO,

Fei Liu

Environmental Technology & Innovation, Journal Year: 2025, Volume and Issue: unknown, P. 104157 - 104157

Published: March 1, 2025

Language: Английский

Citations

0

Study on the Spatial Association Network Structure of Urban Digital Economy and Its Driving Factors in Chinese Cities DOI Creative Commons
Wei Yang,

Meihui Yan,

Xiaohe Wang

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(5), P. 322 - 322

Published: April 27, 2025

The digital economy has become an important engine for global economic development by promoting optimal resource allocation and advancing industrial restructuring. Based on the panel data from 279 prefecture-level cities in China 2012 to 2021, this paper constructs spatial association networks of urban using a modified gravity model analyzes complex network characteristics driving factors growth social analysis methods Quadratic Assignment Procedure (QAP). This study finds that (1) level shows rising trend year displays uneven distribution. (2) Spatial are relatively well-connected, with increasing density stability associations, yet some hierarchical structure remains, overall connectivity still needs be improved. (3) Most east region occupy core positions within network, significantly influencing through “siphon effect”, while central play more “bridge” role network. In contrast, northwest, northeast, southwest regions situated periphery (4) level, informatization technological innovation, urbanization structure, human capital contribute formation economy. these conclusions, specific policy implications future proposed.

Language: Английский

Citations

0

Spatial differences, evolutionary characteristics and driving factors on economic resilience of the construction industry: evidence from China DOI
Zhenshuang Wang,

Tingyu Hu,

Jingkuang Liu

et al.

Engineering Construction & Architectural Management, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 22, 2024

Purpose The sensitivity and fragility of the construction industry’s economic system make resilience industry (ERCI) a key concern for stakeholders decision-makers. This study aims to measure ERCI, identify heterogeneity spatial differences in provide scientific guidance improvement paths industry. It provides foundation implementation policies developing countries future. Design/methodology/approach comprehensive index method, Theil standard deviation ellipse method geographic detector model are used investigate differences, spatiotemporal evolution characteristics influencing factors ERCI from 2005 2020 China. Findings was “high east low west”, Jiangsu has highest value with 0.64. shows wave downward pattern, significant heterogeneity. overall difference is mainly caused by regional contribution rates being higher more than 70%. Besides, between different regions increasing. centered Henan Province, showing clustering trend “northeast-southwest” direction, weakened polarization shrinking distribution range. market size, input level factors, industrial scale main resilience. interaction each factor exhibits an enhanced relationship, including non-linear enhancement dual-factor enhancement, no weakening or independent relationship. Practical implications Exploring driving China, which can crucial insights references stakeholders, authorities decision-makers similar growth leading national economy context areas countries. Originality/value development engine most establishes evaluation on measurement analyzes effects, largest country dynamic perspective. Moreover, it explores multi-factor mechanism formation process theoretical basis empirical support promoting healthy optimizes ways enhance improve ERCI.

Language: Английский

Citations

3